{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "M_BAL.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNBbFbUXrZL7pBO+3eyA7uD",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/M_BAL.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UFKfmq6mbcXe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NDcuDd0tb2zP",
        "colab_type": "text"
      },
      "source": [
        "#Using the technique of Havlena and Odeh the material balance for a gascap drive reservoir can be expressed as :\n",
        "\n",
        "## $F = N (Eo + mEg )$\n",
        "\n",
        "Where, F = Underground Withdrawl, N = OIIP, Eo = Oil Expansion factor, Eg = Gas expansion factor, m = initial downhole gas/oil volume ratio\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V4N7QS1icBWH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JVXSYxGZcKlB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Pb = 3330\n",
        "Pi = 3330\n",
        "P = np.array([3330,3150,3000,2850,2700,2550,2400]) #psi\n",
        "Np = np.array([0, 3.295, 5.903,8.852,11.503,14.513,17.730]) #MMSTB\n",
        "Rp  = np.array([0,1050,1060,1160,1235,1265,1300]) #scf/stb\n",
        "Bo = np.array([1.2511,1.2353,1.2222,1.2122,1.2022,1.1922,1.1822]) #rb/STB\n",
        "Rs = np.array([510,477,450,425,401,375,352]) #scf/stb\n",
        "Bg = np.array([0.00087,0.00092,0.00096,0.00101,0.00107,0.00113,0.00120]) #rb/scf"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CzE1jqMLekhg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame({'P':P,'Np':Np,'Rp':Rp,'Bo':Bo,'Rs':Rs,'Bg':Bg})"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHQ3_ubbe0jx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "66be21ba-9b8a-422c-82ed-dc604a046de6"
      },
      "source": [
        "df"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P</th>\n",
              "      <th>Np</th>\n",
              "      <th>Rp</th>\n",
              "      <th>Bo</th>\n",
              "      <th>Rs</th>\n",
              "      <th>Bg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3330</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>1.2511</td>\n",
              "      <td>510</td>\n",
              "      <td>0.00087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3150</td>\n",
              "      <td>3.295</td>\n",
              "      <td>1050</td>\n",
              "      <td>1.2353</td>\n",
              "      <td>477</td>\n",
              "      <td>0.00092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3000</td>\n",
              "      <td>5.903</td>\n",
              "      <td>1060</td>\n",
              "      <td>1.2222</td>\n",
              "      <td>450</td>\n",
              "      <td>0.00096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2850</td>\n",
              "      <td>8.852</td>\n",
              "      <td>1160</td>\n",
              "      <td>1.2122</td>\n",
              "      <td>425</td>\n",
              "      <td>0.00101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2700</td>\n",
              "      <td>11.503</td>\n",
              "      <td>1235</td>\n",
              "      <td>1.2022</td>\n",
              "      <td>401</td>\n",
              "      <td>0.00107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2550</td>\n",
              "      <td>14.513</td>\n",
              "      <td>1265</td>\n",
              "      <td>1.1922</td>\n",
              "      <td>375</td>\n",
              "      <td>0.00113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2400</td>\n",
              "      <td>17.730</td>\n",
              "      <td>1300</td>\n",
              "      <td>1.1822</td>\n",
              "      <td>352</td>\n",
              "      <td>0.00120</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      P      Np    Rp      Bo   Rs       Bg\n",
              "0  3330   0.000     0  1.2511  510  0.00087\n",
              "1  3150   3.295  1050  1.2353  477  0.00092\n",
              "2  3000   5.903  1060  1.2222  450  0.00096\n",
              "3  2850   8.852  1160  1.2122  425  0.00101\n",
              "4  2700  11.503  1235  1.2022  401  0.00107\n",
              "5  2550  14.513  1265  1.1922  375  0.00113\n",
              "6  2400  17.730  1300  1.1822  352  0.00120"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XGZkZ7Ate5p8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "6d0d874e-0990-4f9e-c50c-d0b33269e5d1"
      },
      "source": [
        "#Let's See the variation of all these parameters with Pressure for self analysis\n",
        "plt.figure(figsize=(6,4))\n",
        "\n",
        "# plt.plot(P,Np*1000,label='Np vs P')\n",
        "# plt.plot(P,Bo,label='Bo vs P')\n",
        "plt.plot(P,Rp,label='Rp vs P')\n",
        "plt.plot(P,Rs,label='Rs vs P')\n",
        "# plt.plot(P,Bg,label='Bg vs P')\n",
        "\n",
        "plt.legend(loc='best')\n",
        "plt.grid()\n"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ptHM1rABe8UP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "65a452f0-a5b5-4b0b-9bde-605772ed94d1"
      },
      "source": [
        "#Let's See the variation of all these parameters with Pressure for self analysis\n",
        "plt.figure(figsize=(6,4))\n",
        "\n",
        "# plt.plot(P,Np*1000,label='Np vs P')\n",
        "plt.plot(P,Bo,label='Bo vs P')\n",
        "# plt.plot(P,Rp,label='Rp vs P')\n",
        "# plt.plot(P,Rs,label='Rs vs P')\n",
        "plt.plot(P,Bg,label='Bg vs P')\n",
        "\n",
        "plt.legend(loc='best')\n",
        "plt.grid()\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vJ8QwD51hB1i",
        "colab_type": "text"
      },
      "source": [
        "#Problem statement continued: \n",
        "The size of the gascap is uncertain with the best estimate, based on geological\n",
        "information, giving the value of m = 0.4. \n",
        "What's the impact of m on N, show that with different values of m, how the estimated N varies. \n",
        "##Find the correct m and N"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y5Eq9eqmgXtC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "f011503a-a223-4480-90cc-b57d4e62d1dd"
      },
      "source": [
        ""
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P</th>\n",
              "      <th>Np</th>\n",
              "      <th>Rp</th>\n",
              "      <th>Bo</th>\n",
              "      <th>Rs</th>\n",
              "      <th>Bg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3330</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>1.2511</td>\n",
              "      <td>510</td>\n",
              "      <td>0.00087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3150</td>\n",
              "      <td>3.295</td>\n",
              "      <td>1050</td>\n",
              "      <td>1.2353</td>\n",
              "      <td>477</td>\n",
              "      <td>0.00092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3000</td>\n",
              "      <td>5.903</td>\n",
              "      <td>1060</td>\n",
              "      <td>1.2222</td>\n",
              "      <td>450</td>\n",
              "      <td>0.00096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2850</td>\n",
              "      <td>8.852</td>\n",
              "      <td>1160</td>\n",
              "      <td>1.2122</td>\n",
              "      <td>425</td>\n",
              "      <td>0.00101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2700</td>\n",
              "      <td>11.503</td>\n",
              "      <td>1235</td>\n",
              "      <td>1.2022</td>\n",
              "      <td>401</td>\n",
              "      <td>0.00107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2550</td>\n",
              "      <td>14.513</td>\n",
              "      <td>1265</td>\n",
              "      <td>1.1922</td>\n",
              "      <td>375</td>\n",
              "      <td>0.00113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2400</td>\n",
              "      <td>17.730</td>\n",
              "      <td>1300</td>\n",
              "      <td>1.1822</td>\n",
              "      <td>352</td>\n",
              "      <td>0.00120</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      P      Np    Rp      Bo   Rs       Bg\n",
              "0  3330   0.000     0  1.2511  510  0.00087\n",
              "1  3150   3.295  1050  1.2353  477  0.00092\n",
              "2  3000   5.903  1060  1.2222  450  0.00096\n",
              "3  2850   8.852  1160  1.2122  425  0.00101\n",
              "4  2700  11.503  1235  1.2022  401  0.00107\n",
              "5  2550  14.513  1265  1.1922  375  0.00113\n",
              "6  2400  17.730  1300  1.1822  352  0.00120"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V5rJB-yuiBtZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Eo = Bo - Bo[0] + (Rs[0] - Rs)*Bg"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C6Ynnw0AiYqF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "F = Np*(Bo + (Rp - Rs)*Bg) #MMrb"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SOwBP3P2iktq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Eg = Bo[0]*((Bg/Bg[0])-1)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EkyilntRi9HA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_MBE = pd.DataFrame({'P':P,'F':F,'Eo':Eo,'Eg':Eg})"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BsuTtigEji2Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "1ae52cba-9b5d-49c5-f5ea-95d85fc0fc80"
      },
      "source": [
        "df_MBE"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P</th>\n",
              "      <th>F</th>\n",
              "      <th>Eo</th>\n",
              "      <th>Eg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3330</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3150</td>\n",
              "      <td>5.807306</td>\n",
              "      <td>0.01456</td>\n",
              "      <td>0.071902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3000</td>\n",
              "      <td>10.671443</td>\n",
              "      <td>0.02870</td>\n",
              "      <td>0.129424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2850</td>\n",
              "      <td>17.301677</td>\n",
              "      <td>0.04695</td>\n",
              "      <td>0.201326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2700</td>\n",
              "      <td>24.093954</td>\n",
              "      <td>0.06773</td>\n",
              "      <td>0.287609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2550</td>\n",
              "      <td>31.898123</td>\n",
              "      <td>0.09365</td>\n",
              "      <td>0.373892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2400</td>\n",
              "      <td>41.130054</td>\n",
              "      <td>0.12070</td>\n",
              "      <td>0.474555</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      P          F       Eo        Eg\n",
              "0  3330   0.000000  0.00000  0.000000\n",
              "1  3150   5.807306  0.01456  0.071902\n",
              "2  3000  10.671443  0.02870  0.129424\n",
              "3  2850  17.301677  0.04695  0.201326\n",
              "4  2700  24.093954  0.06773  0.287609\n",
              "5  2550  31.898123  0.09365  0.373892\n",
              "6  2400  41.130054  0.12070  0.474555"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zsIDPraCjkGQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# now let's assume 3 values of m - 0.4,0.5,0.6\n",
        "m1 = 0.4 \n",
        "m2 = 0.5\n",
        "m3 = 0.6"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h9AHPDZ1kHpO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Eo_plus_mEg_1 = Eo + m1*Eg\n",
        "Eo_plus_mEg_2 = Eo + m2*Eg\n",
        "Eo_plus_mEg_3 = Eo + m3*Eg"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hWGd_uhrkqhq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_MBE['RHS_1'] = Eo_plus_mEg_1\n",
        "df_MBE['RHS_2'] = Eo_plus_mEg_2\n",
        "df_MBE['RHS_3'] = Eo_plus_mEg_3"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wv45xcznlG5W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_MBE = df_MBE.drop('Eo + mEg',axis=1)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sO2D0OQNlIpt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "d01e092c-4ca0-4f10-bd78-36e76604a54d"
      },
      "source": [
        "df_MBE"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P</th>\n",
              "      <th>F</th>\n",
              "      <th>Eo</th>\n",
              "      <th>Eg</th>\n",
              "      <th>RHS_1</th>\n",
              "      <th>RHS_2</th>\n",
              "      <th>RHS_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3330</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3150</td>\n",
              "      <td>5.807306</td>\n",
              "      <td>0.01456</td>\n",
              "      <td>0.071902</td>\n",
              "      <td>0.043321</td>\n",
              "      <td>0.050511</td>\n",
              "      <td>0.057701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3000</td>\n",
              "      <td>10.671443</td>\n",
              "      <td>0.02870</td>\n",
              "      <td>0.129424</td>\n",
              "      <td>0.080470</td>\n",
              "      <td>0.093412</td>\n",
              "      <td>0.106354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2850</td>\n",
              "      <td>17.301677</td>\n",
              "      <td>0.04695</td>\n",
              "      <td>0.201326</td>\n",
              "      <td>0.127481</td>\n",
              "      <td>0.147613</td>\n",
              "      <td>0.167746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2700</td>\n",
              "      <td>24.093954</td>\n",
              "      <td>0.06773</td>\n",
              "      <td>0.287609</td>\n",
              "      <td>0.182774</td>\n",
              "      <td>0.211535</td>\n",
              "      <td>0.240296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2550</td>\n",
              "      <td>31.898123</td>\n",
              "      <td>0.09365</td>\n",
              "      <td>0.373892</td>\n",
              "      <td>0.243207</td>\n",
              "      <td>0.280596</td>\n",
              "      <td>0.317985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2400</td>\n",
              "      <td>41.130054</td>\n",
              "      <td>0.12070</td>\n",
              "      <td>0.474555</td>\n",
              "      <td>0.310522</td>\n",
              "      <td>0.357978</td>\n",
              "      <td>0.405433</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      P          F       Eo        Eg     RHS_1     RHS_2     RHS_3\n",
              "0  3330   0.000000  0.00000  0.000000  0.000000  0.000000  0.000000\n",
              "1  3150   5.807306  0.01456  0.071902  0.043321  0.050511  0.057701\n",
              "2  3000  10.671443  0.02870  0.129424  0.080470  0.093412  0.106354\n",
              "3  2850  17.301677  0.04695  0.201326  0.127481  0.147613  0.167746\n",
              "4  2700  24.093954  0.06773  0.287609  0.182774  0.211535  0.240296\n",
              "5  2550  31.898123  0.09365  0.373892  0.243207  0.280596  0.317985\n",
              "6  2400  41.130054  0.12070  0.474555  0.310522  0.357978  0.405433"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pnPpjZT8lxxV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "outputId": "5a1dbf76-71bd-4af5-8fa7-d3b15b63d1af"
      },
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "\n",
        "plt.plot(df_MBE['RHS_1'],F,label='Theoritically best, m=0.4')\n",
        "plt.plot(df_MBE['RHS_2'],F,label='m=0.5')\n",
        "plt.plot(df_MBE['RHS_3'],F,label='m=0.6')\n",
        "\n",
        "plt.grid()\n",
        "plt.legend(loc='best')\n",
        "\n",
        "plt.xlabel('Eo + mEg')\n",
        "plt.ylabel('F (underground withdrawl)')\n",
        "plt.title('Slope gives us The N (Oil in Place')"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Slope gives us The N (Oil in Place')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3AQEOuh8oJs-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "slope, intercept = np.polyfit(df_MBE['RHS_1'],F, 1)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sxqb2ZWYpKko",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "92b73a34-5c28-48bb-98d8-b58b799cc3bd"
      },
      "source": [
        "print(slope)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "131.77712071961054\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tVhWG4dwpMaY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "slope2, intercept2 = np.polyfit(df_MBE['RHS_2'],F, 1)\n",
        "slope3, intercept3 = np.polyfit(df_MBE['RHS_3'],F, 1)\n",
        "slope1, intercept = np.polyfit(df_MBE['RHS_1'],F, 1)"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H0arcNSlpTlb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "6728705c-eb88-4dfc-ebb8-62b7e769d9df"
      },
      "source": [
        "print(f'1. The Estimate of N (Oil in place) with m = 0.4 is {slope1} MMSTB')\n",
        "print(f'2. The Estimate of N (Oil in place) with m = 0.5 is {slope2} MMSTB')\n",
        "print(f'2. The Estimate of N (Oil in place) with m = 0.6 is {slope3} MMSTB')"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1. The Estimate of N (Oil in place) with m = 0.4 is 131.77712071961054 MMSTB\n",
            "2. The Estimate of N (Oil in place) with m = 0.5 is 114.37046619758794 MMSTB\n",
            "2. The Estimate of N (Oil in place) with m = 0.6 is 101.024892196518 MMSTB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yl6DJZpLsQLX",
        "colab_type": "text"
      },
      "source": [
        "#Method 2 : Finding N with another plots to verify results\n",
        "\n",
        "## $ \\frac{F}{Eo} = N + mN \\frac{Eg}{Eo}$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bpn5iJWtpW6s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_MBE['F/Eo'] = df_MBE['F']/df_MBE['Eo']\n",
        "df_MBE['Eg/Eo'] = df_MBE['Eg']/df_MBE['Eo']"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bNY15Lq6xNgF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mn, n = np.polyfit(df_MBE['Eg/Eo'].iloc[1:],df_MBE['F/Eo'].iloc[1:], 1)"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "04wn7iB6uMiK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "outputId": "9a1af55d-356b-401e-84a9-fb872d44afb8"
      },
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "\n",
        "sns.regplot(x='Eg/Eo',y='F/Eo',data=df_MBE,label = 'F/Eo vs Eg/Eo',line_kws={'label':f'y = {n} + {mn}x'},color='brown')\n",
        "\n",
        "\n",
        "plt.grid()\n",
        "plt.legend(loc='best')\n",
        "plt.title('A history match/verification of results')"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'A history match/verification of results')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j9hoWTnwqIiA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mn, n = np.polyfit(df_MBE['Eg/Eo'].iloc[1:],df_MBE['F/Eo'].iloc[1:], 1)"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "naANEoHdqKex",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "6c92173f-69d0-4d0e-8c25-993c1bda4598"
      },
      "source": [
        "print(f'1.Based on the practical production Data, The correct value of N(OIP) is: {n}')\n",
        "print(f'2.Based on the practical production Data, The correct value of m is: {mn/n}')"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.Based on the practical production Data, The correct value of N(OIP) is: 108.70272737219946\n",
            "2.Based on the practical production Data, The correct value of m is: 0.5412020027520338\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LCaBaChFqsq4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H_ijQAHWqvI2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}